code: def _partition_estimators(n_estimators, n_jobs):
      """Private function used to partition estimators between jobs."""
      # Compute the number of jobs
      n_jobs = min(effective_n_jobs(n_jobs), n_estimators)
  
      # Partition estimators between jobs
      n_estimators_per_job = np.full(
          n_jobs, n_estimators // n_jobs, dtype=int
      )
      n_estimators_per_job[: n_estimators % n_jobs] += 1
      starts = np.cumsum(n_estimators_per_job)
  
      return (
          n_jobs,
          n_estimators_per_job.tolist(),
          [0] + starts.tolist(),
      )
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from typing import List
  import numpy
  from joblib import effective_n_jobs
  from ..base import BaseEstimator, MetaEstimatorMixin, clone, is_classifier, is_regressor
  from ..utils import Bunch, _print_elapsed_time, check_random_state, deprecated
  from ..utils.metaestimators import _BaseComposition
label_desc: Use image node_id 3 for calling a global function with example usage: _partition_estimators(n_estimators, n_jobs) and returns: n_jobs,  n_estimators_per_job,  unknown